Using NVivo for Mixed Methods Research

Humans are inherently complex. Research with human subjects requires patience, attention to detail, and a love of intersectionality.

NVivo helps one harness their inner zen, expand their ability to deepen analysis while also increasing sources, and unearth nuances that lie beyond the naked eye.

As a researcher and program evaluator, mixed methodologies in research design, execution, and analysis are critical to my approach. NVivo has proven particularly helpful in quantifying findings from qualitative interviews and focus groups, and pairing “big data” with qualitative results.

Quantifying Qualitative Interviews & Focus Groups

Qualitative data is complex and often difficult to quantify. NVivo helps breakdown this richly detailed information into themes, sub-themes, and even micro-points of analysis.

I generally use a three-step process for coding interviews and focus groups in NVivo.

Before I start, interviews are recorded, transcribed, and uploaded into the project interface. Codes, or “nodes,” are also identified using key research questions.

Code themes using broad strokes at the source level.

Code each interview or focus group answer with the appropriate nodes. This coding should be at either the entire-answer or individual level. Meaning, code each unique and complete answer with the necessary nodes. If more than one person answered a question in the same focus group, code each person’s answer individually.

Perform light text mining using special word and word-sequence queries.

Locating Cogent Analysis

True analysis occurs at the intersection of codes.

For a study of livelihoods and refugee men living in camp and urban settings across the Middle East, we would find codes often intersecting at ‘man’, ‘camp’ or ‘urban,’ specific country, and income level or livelihood source.

Our most cogent and meaningful analysis would occur at the intersection rather than individual codes. Indeed, for this study we would be most interested in the relationship between setting, location, and livelihood, not simply the setting or the livelihood alone.

Some helpful tips for cogent analysis:

Apply a gender-sensitive lens to every piece of work you do. Men/boys and women/girls experience the world differently. Disaggregating data, whether qualitative or quantitative, will highlight these unique experiences.

Look for words or phrases that repeatedly appear, especially if they are outside your original research questions. Interviews and focus groups may present unusual and consistent responses to questions. This is particularly helpful for program evaluation.

Code in research groups to help identify areas of difficult interpretation. More eyes means more ideas and less researcher bias.

Pairing with Big Data

Big data, in a sense, is the global counterpart to what is NVivo’s local analysis.

I use STATA or R software packages to find trends in complimentary dataset(s) that mirror my qualitative findings.

This can be simple frequency and descriptive analyses or in-depth multivariate regressions. Either way, I utilize my results from NVivo as foundation for big data exploration. This process helps to pair qualitative and quantitative finds, and marry all results to my research questions.

Another helpful tip: Coding interviews by question, with a code for ‘quote,’ can later speed up your search for particularly persuasive quotes, which are useful to break up large sections of quantitative data analysis; again, using big data as global and qualitative data as local.

Mixed Methods Research IRL (In Real Life)

As a researcher and program evaluator, I have the privilege of supporting the incredibly meaningful work of NGOs with both local and international programs.

Recently, as a consultant to the gender and disability project team at the Women’s Refugee Commission, I provided mixed-methodological support for a study involving interviews and focus groups conducted with refugees living in Burundi, Ethiopia, and Jordan. Our team coded qualitative data using NVivo and complimented these findings with quantitative analysis of the GBV-IMS dataset using STATA. Our forthcoming report highlights trends at the local and global levels, as well as unexpected developments around refugees’ expressed need for increased confidentiality.

Prior to this project, I used NVivo to analyze interviews I conducted with female ex-combatants and peacebuilders in Tajikistan.

Because NVivo allows for a greater breadth and depth of coding than other more rudimentary processes (read: by hand), I was able to better integrate audio, visual, and transcribed sources in my research.

Mixed-methodological research is a truly holistic approach to understanding the experiences and needs of human subjects. NVivo helps to broaden sources and deepen analysis.

Finally, employing the processes and tips I’ve identified above can help researchers work through particularly difficult questions around data interpretation, research ethics, and reduction of researcher bias.